Norbert
Eke
DATA SCIENTIST
 +1 250 878 7547
Education
|
 norberteke@protonmail.com
|  norberte.github.io
|  GitHub
Master of Computer Science - Specialization in Data Science
CARLETON UNIVERSITY
Graduated with a GPA of 11.4/ 12. Master’s Thesis in NLP, Machine Learning, and Data Mining
Bachelor of Science - Honours in Computer Science, Minor in Data Science
UNIVERSITY OF BRITISH COLUMBIA - OKANAGAN
Graduated with a GPA of 3.8/ 4.0. Honours Thesis in NLP, and Machine Learning
|  LinkedIn
Ottawa, ON, Canada
Sept 2018 - June 2020
Kelowna, BC, Canada
Sept 2014 - June 2018
Experience
Data Scientist
Ottawa, ON, Canada
HEALTH CANADA
February 2021 - present
• Developing NLP & predictive modelling for Health Canada’s Risk Intelligence and Strategic Operations team
• Responsible for researching and implementing text processing, phrase detection, keyword extraction, text summarization and predictive machine learning models for health product compliance verification data
Graduate Researcher and Teaching Assistant
Ottawa, ON, Canada
CARLETON UNIVERSITY
Sept 2018 - April 2020
• Hosted office hours & graded assignments for the Introduction to Databases and Distributed Computing courses
• Conducted research on Software Developer Expertise Learning in Prof. Olga Baysal’s Software Analytics lab
• Applied the research to recruitment and talent search by determining candidate expertise in data-driven ways
Data Scientist Intern
Ottawa, ON, Canada
NATIONAL RESEARCH COUNCIL CANADA (NRC)
May 2019 - August 2019
• Successfully delivered a fully working novel solution for a government client with NRC’s Data Analytics Centre
• Performed various time series analysis of textual data using Bayesian changepoint detection algorithms
• Delivered an extensive report on the analysis results, presented findings to the client, and shipped the source code
Undergraduate Researcher
Kelowna, B.C, Canada
UNIVERSITY OF BRITISH COLUMBIA - OKANAGAN
May 2017 - August 2017
• Conducted research on feature-based opinion mining and delivered a presentation about our findings – GitHub
• Developed automated opinion detection from reviews about product features using Machine Learning
Skills
Interpersonal Skills
Teamwork, Intellectual Curiosity, Excellent Leadership & Communication Skills, Time
Management, Motivation, Optimism, Analytical Thinking, Data-Driven Problem Solving
Programming
PYTHON, R, SQL, JAVA, Object-Oriented Programming, Data Structures, Algorithmic Thinking
Software Engineering
Experienced with Client-Server Applications (projects), Algorithm Implementation, CI & CD,
Git, Deployment, Unit Testing, Agile Development, Writing Reports & Documentation
Cloud & Deployment
AWS, Azure, BigQuery, Flask, Docker, Experience with ETL pipelines
ML Platforms and Libraries TensorFlow, Keras, SciKit-Learn, Imbalanced-Learn, tidyverse, StatsModels, PyCaret, SpaCy,
Seaborn, Numpy, Pandas, Matplotlib, SciPy, SQLAlchemy, Scikit-Optimize, Gensim, NLTK
Machine Learning
Supervised & Unsupervised: Traditional ML (Random Forest, k-NN, SVM), Outlier Detection,
Time-series Forecasting, Hyper-param. Optimization, Regression, Classification, Clustering
Deep Learning
Neural Nets for NLP and Computer Vision Applications using CNN, RNN, LSTM, GRU, GAN
Statistics
Descriptive Statistics, Probability Theory, Bayesian Statistics, Time Series Analysis, EDA,
Statistical Modeling & Inference, Dimensionality Reduction (t-SNE, PCA), Hypothesis Testing
Data Analysis
Extensive experience with Data Cleansing, Wrangling, Visualization (Tableau, Ggplot), A/B
testing, Predictive Modeling, Data Mining, Handling Unstructured Data, Algorithm Design
Databases
Extensive experience with SQL, Architecture Design, MySQL, PostgreSQL, NoSQL
Projects
DATA SCIENCE AND MACHINE LEARNING
2017-2020
• F1 Data Analysis: Analyzed previous Turkish GP race lap times, and visualizing race pace trends across different years
• Completed an 8-month Undergraduate Thesis project on Detection of Sexual Predatory Behavior in Chat-rooms
• Provided statistical consulting for the Statistical Society of Canada and improved their conference schedule
• Performed Unsupervised Feature Selection, Outlier Detection, Time Series Analysis as professional development
DEEP LEARNING
2019-2020
• F1 Side-Project: Built a binary classifier using LSTM models that will predict if a pit-stop will happen the next race lap
• Completed TensorFlow in Practice Specialization course; fitted MLP, CNN, RNN & LSTM models for various tasks
• Conducted research in Anomaly Detection using Generative Adversarial Networks; available on GitLab
• Trained models for Text Entailment and Semantic Relatedness NLP task, available on GitHub